Traditional research in neuroscience has studied brain function by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. However, the brain is constantly active. This intrinsic activity is synchronous across widely distributed regions known as resting- networks (RSNs). Recent work has established that resting state networks represent a fundamental aspect of brain function at the systems level. Each RSN is associated with a specific function such as vision, motor control and language. However, the physiological underpinnings of RSNs are incompletely understood. RSNs have thus far been studied primarily with non-invasive neuroimaging techniques (specifically functional MRI). The objective of this proposal is to combine functional MRI and invasive electrophysiology to investigate the electrophysiological correlates of resting state fMRI networks. This project will investigate the correspondence between electrocorticographic (ECoG, brain surface potential) recordings and RSNs in patients undergoing invasive monitoring prior to surgical treatment of epilepsy. The first aim will compare correlation patterns of oscillations of brain surface potentials to the topography of RSNs as measured by fMRI. The second aim will compare correlation patterns of fluctuations in the power of oscillations to RSNs. This investigation will test the hypothesis that there is frequency specificity in these relationships that varies by RSN. Aim 3 will investigate the relationship between ongoing brain activity and responses induced by similar task performance. The proposed work represents an investigation of basic brain-behavior processes. It is known that topographies of RSNs are altered in diseases such as schizophrenia, major depression, and autism, but the practical implications of these observations remain to be determined. By integrating ECoG and fMRI, this work establishes and develops novel methods (in resting-state electrophysiology) to better understand intrinsic brain activity in health and disease. A better understanding of RSNs will certainly lead to better understanding of mental health disorders and potentially better diagnostic criteria and treatments.